Title
A fuzzy-autoregressive model of daily river flows
Abstract
A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the nonlinear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560km^2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.
Year
DOI
Venue
2012
10.1016/j.cageo.2012.02.031
Computers & Geosciences
Keywords
Field
DocType
fuzzy model,river volturno,low river flow,fuzzy-autoregressive model,daily flow series,river basin,daily river flow,autoregressive model,ar model,model training,time series analysis
Meteorology,Time series,Autoregressive model,Base flow,Drainage basin,Computer science,Hydrology,Fuzzy logic,Surface runoff,Runoff model,Inflow
Journal
Volume
ISSN
Citations 
43,
0098-3004
0
PageRank 
References 
Authors
0.34
5
1
Name
Order
Citations
PageRank
Roberto Greco153.40